The present invention relates to business to business market price control and management systems. More particularly, the present invention relates to systems and methods for optimizing prices in a business to business market setting wherein an optimal price change is determined according to business strategy and objectives.
There are major challenges in business to business (hereinafter “B2B”) markets which hinder the effectiveness of classical approaches to price optimization.
For instance, in B2B markets, a small number of customers represent the lion's share of the business. Managing the prices of these key customers is where most of the pricing opportunity lies. Also, B2B markets are renowned for being data-poor environments. Availability of large sets of accurate and complete historical sales data is scarce.
Furthermore, B2B markets are characterized by deal negotiations instead of non-negotiated sale prices (prevalent in business to consumer markets). There is no existing literature on optimization of negotiation terms and processes, neither at the product/segment level nor at the customer level.
Finally, B2B environments suffer from poor customer segmentation. Top-down price segmentation approaches are rarely the answer. Historical sales usually exhibit minor price changes for each customer. Furthermore, price bands within customer segments are often too large and customer behavior within each segment is non-homogeneous.
Product or segment price optimization relies heavily on the quality of the customer segmentation and the availability of accurate and complete sales data. In this context, price optimization makes sense only (i) when price behavior within each customer segment is homogeneous and (ii) in the presence of data-rich environments where companies sales data and their competitors' prices are readily available. These conditions are met almost exclusively in business to consumer (hereinafter “B2C”) markets such as retail, and are rarely encountered in B2B markets.
On the other hand, customer price optimization relies heavily on the abundance of data regarding customers' past behavior and experience, including win/loss data and customer price sensitivity. Financial institutions have successfully applied customer price optimization in attributing and setting interest rates for credit lines, mortgages and credit cards. Here again, the aforementioned condition is met almost exclusively in B2C markets.
There are three major types of price optimization solutions in the B2B marketplace: revenue/yield management, price testing and highly customized optimization solutions.
Revenue/yield management approaches were initially developed in the airline context, and were later expanded to other applications such as hotel revenue management, car rentals, cruises and some telecom applications (e.g. bandwidth pricing). These approaches are exclusively concerned with perishable products (e.g. airline seats) and are not pricing optimization approaches per se
Price testing approaches attempt to learn and model customer behavior dynamically by measuring customer reaction to price changes. While this approach has been applied rather successfully in B2C markets, where the benefits of price optimization outweigh the loss of a few customers, its application to B2B markets is questionable. No meaningful customer behavior can be modeled without sizable changes in customer prices (both price increases and decreases). In B2B markets, where a small fraction of customers represent a substantial fraction of the overall business, these sizable price-changing tests can have adverse impact on business. High prices can drive large customers away with potentially a significant loss of volume. Low prices on the other hand, even for short periods of time, can dramatically impact customer behavior, increase customers' price sensitivities and trigger a more strategic approach to purchasing from the customers' side.
Finally, in B2B markets, highly customized price optimization solutions have been proposed. These solutions have had mixed results. These highly customized price optimization solutions require significant consulting effort in order to address companies' unique situations including cost structure, customer and competitor behavior, and to develop optimization methods that are tailored to the type of pricing data that is available. Most of the suggested price changes from these solutions are not implemented. Even when they are implemented, these price changes tend not to stick. Furthermore, the maintenance of such pricing solutions usually requires a lot of effort. This effort includes substantial and expensive on-going consulting engagements with the pricing companies.
These solutions have failed primarily because of the lack of reliable price control and management systems. In fact, in B2B markets, reliable price control and management systems may be significantly more complex and more important than price optimization modules.
There remains a need for effective price control and management systems coupled with straightforward price optimization modules which can perform price changes in an effective manner, and can measure the performance of these price changes across the sales and marketing organizations, and across product and customer segments, both for existing business (repeat business) and new business.
Furthermore, instead of developing highly customized company-specific price optimization solutions, there remains a need for scalable and customizable price optimization solutions that vary by industry vertical.
Price control and management systems are employed in business in an effort to gain efficiencies and increase margin. Businesses employ a myriad of enterprise resource planning tools in order to manage and control pricing processes.
In particular, in the context of business to business markets, effective price modeling and optimization schemes have been elusive given the scarcity of sales data and the relatively small pool of available customers. In this environment, it is important to include all available relevant data, including competitive behavior data, in order to develop robust price modeling and optimization schemes. It is also important to continuously loop back to update and calibrate the price modeling and optimization schemes with new sales data generated from deals consummated with the benefit of the instant price modeling and optimization schemes.
As such, methods for generating effective business to business price optimization modules, as well as systems and methods for incorporating business to business price modeling and optimization modules into an integrated price control and management system in order to optimize a selected business objective, may be desirable to achieve system-wide price management efficiency.
In view of the foregoing, Systems and Methods For Business to Business Price Modeling Using Continuous Learning and Calibration are disclosed.